Introduction

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Figure

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Figure

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Introduction

This report aims to analyze the changes in the victims of various crimes in Australia from 2010 to 2019. We hope to find some problems by analyzing the data: From the perspective of state, gender and age, how have the victims of various crimes changed in the past ten years?

The report is based on victim data from the Australian Bureau of Statistics. It is a data set generated by regular judicial and criminal investigations and aims to provide the number and rate of victims across Australia in terms of state, gender and age. All ABS data displayed on this website are provided under the Creative Commons Attribution 4.0 international license. It is open data and can be shared freely and adapted for any purpose.

Researchers

Team12 Researchers:

  • Kexin Xu
  • Yiru Wang
  • Ruiqi Tang

State

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Chart 1

Comparison of the number of victims of various crimes in each state in the same year

Comparison of the number of victims of various crimes in each state in the same year

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Chart 2

Rate of change in the number of victims of various crimes by state

Rate of change in the number of victims of various crimes by state

Chart 3

Ten-year rate of change in the number of victims of various crimes by state
State robbery_rate robbery_change Sexual assault_rate Sexual assault_change Murder_rate Murder_change
Capital -19.867550 -30 69.430052 134 -100.000000 -3
NewSouthWales -43.953488 -945 51.451369 3740 4.109589 3
Northern Territory 43.478261 20 7.926829 26 -9.090909 -1
Queensland 73.246753 564 14.626091 620 -2.083333 -1
South Australia -34.482759 -180 13.719736 187 -33.333333 -5
Tasmania 1.176471 1 9.604520 17 -33.333333 -2
Victoria 40.751043 586 60.622761 2200 19.148936 9
Western Australia 13.358071 72 67.412334 1115 -10.000000 -3
Ten-year rate of change in the number of victims of various crimes by state
State extortion_rate extortion_change Manslaughter_rate Manslaughter_change Kidnapping_rate Kidnapping_change
Capital Inf 6 NaN 0 Inf 7
NewSouthWales -46.24277 -80 18.18182 2 -31.610942 -104
Northern Territory Inf 3 -100.00000 -3 NaN 0
Queensland 108.69565 50 -57.14286 -4 -13.235294 -9
South Australia 96.77419 30 NaN 0 -9.230769 -6
Tasmania NaN 0 NaN 0 Inf 3
Victoria 43.06569 59 366.66667 11 36.206897 42
Western Australia 20.00000 18 0.00000 0 21.052632 4

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Chart 4

The rate of change in the rate of various crime victims by state

The rate of change in the rate of various crime victims by state

Chart 5

A 10-year change in the proportion of victims of sexual assault
State 2010-2011 rate_change
NewSouthWales 34.4 33.8249754
Victoria 21.9 32.9323308
Queensland -0.8 -0.8316008
South Australia 4.7 5.6085919
Western Australia 33.4 46.2603878
Tasmania 1.5 4.3103448
Northern Territory 23.3 43.7148218
Capital 23.3 43.7148218

Gender

Column

Total number by gender

Analysis

  • On the whole, it shows an upward trend year by year recently.

  • The number of female victims is much higher than that of male victims, almost twice.

  • With the exception of sexual assault and kidnapping, the majority of victims are men.

  • The vast majority of cases are sexual assaults.

  • The number of robberies among the remaining crime categories is also considerable. (Including both armed and unarmed)

  • Possible reasons:

    • Women generally have stronger safety awareness
    • Women are less likely to be seen alone at night in areas where they are likely to be robbed

According to statistics of gender-specific crimes in various countries, male victims are the majority in almost all crimes except for sex-related crimes in which more women are victims.

Column

Number by gender by crime type

Sexual assault proportion

Sexual Assault Rate
Gender Year sa_rate
Female 2010 81.14353
Female 2011 80.92590
Female 2012 81.71220
Female 2013 83.46100
Female 2014 85.77439
Female 2015 87.63073
Female 2016 87.29571
Female 2017 88.86702
Female 2018 88.74093
Female 2019 87.99638
Male 2010 21.67991
Male 2011 23.79634
Male 2012 27.87459
Male 2013 30.70995
Male 2014 34.46328
Male 2015 38.35306
Male 2016 38.22735
Male 2017 39.55563
Male 2018 35.52154
Male 2019 33.06397

Take sexual assault, female victims of sexual assault make up a very high proportion of total female victims.

Column

Rate by gender

Analysis

  • Rate is calculated by victim number divided by 100,000 persons.
  • Show an upward pattern over years.
  • Higher than 0.15% in recent years which means 3 out of every 2,000 people have been sexually assaulted.
  • Possible reasons:
    • Women are physically vulnerable
    • Men are more sexually impulsive
    • Many women may choose not to report the crime
      • relationship to the offender
      • confidence in the justice system
      • fear of revenge
      • public opinion

ABS personal safety survey also measures the number of women who contacted the police about the most recent incident within the last 10 years. Only 13.4% of women did so.

Age

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Age Group

I divided age into four groups

  • 0 -19 years old Teenage

  • 20–34 years old Audlt

  • 35–54 years old Elder audlt

  • 55 years and over Elder

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Trend

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Analysis

From graph, we can see that the trend for both teenage and adult is very similar. it has a decrease trend from 2010 to 2015. Then, it has a increse trend from 2015 to 2019. For the elder adult and elder, they have increase trend for all year.

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Age and Class

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Analysis

From graph. we can see that 20-54 years age are the largest people for criminal record. The 55 year and over are the smallest group.

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---
title: "Report"
author:
- familyname: Wang
  othernames: Yiru
  address: Monash University
  email: "ywan0553@student.monash.edu"
  correspondingauthor: true
  qualifications: section1
- familyname: Xu
  othernames: Kexin
  address: Monash University
  email: "kxuu0029@student.monash.edu"
  qualifications: section2
  correspondingauthor: true
- familyname: Tang
  othernames: Ruiqi
  address: Monash University
  email: "rtan00062student.monash.edu"
  correspondingauthor: true
  qualifications: section3
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: scroll
    source_code: embed
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(fig.align = "center")
```



```{r, echo = FALSE, message = FALSE, warning = FALSE}
# Libraries
library(flexdashboard)
library(tinytex)
library(gridExtra)
library(tidyverse)
library(readr)
library(bookdown)
library(knitr)
library(plotly)
library(kableExtra)
library(readxl)
```

Introduction
======================

Row
-------------------------------------

### Figure

![avatar](https://www.indianfolk.com/wp-content/uploads/2018/08/crime.jpg)

### Figure  

![avatar](https://9q6pu33arq33jokx6qglbp6n-wpengine.netdna-ssl.com/wp-content/uploads/2018/04/ai-for-crime-prevention-and-detection-5-current-applications-1045x471.png)

Row
-------------------------------------

### Introduction{data-width=500}
  
This report aims to analyze the changes in the victims of various crimes in Australia from 2010 to 2019. We hope to find some problems by analyzing the data: From the perspective of state, gender and age, how have the victims of various crimes changed in the past ten years?


The report is based on [victim data](https://www.abs.gov.au/statistics/people/crime-and-justice/recorded-crime-victims/latest-release) from the [Australian Bureau of Statistics](https://www.abs.gov.au/). It is a data set generated by regular judicial and criminal investigations and aims to provide the number and rate of victims across Australia in terms of state, gender and age. All ABS data displayed on this website are provided under the Creative Commons Attribution 4.0 international license. It is open data and can be shared freely and adapted for any purpose.

  
### Researchers{data-width=250}  
 

*Team12  Researchers*:  

- Kexin Xu  
- Yiru Wang  
- Ruiqi Tang  






State {data-icon="fa-globe"}
=============================
 

Row {data-height=1000}
-------------------------------------
    
### Chart 1

```{r NewSouthWales}
NewSouthWales <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 3,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "NewSouthWales")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r NewSouthWales-rate,message=FALSE}
NewSouthWales_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 3,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "NewSouthWales")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```



```{r Victoria}
Victoria <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 4,range = "A5:AB28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Victoria")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Victoria_rate,message=FALSE}
Victoria_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 4,range = "A29:AB37")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
 mutate(State = "Victoria")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```



```{r Queensland}
Queensland <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 5,range = "A5:AB28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Queensland")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Queensland_rate,message=FALSE}
Queensland_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 5,range = "A29:AB37")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "Queensland")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```



```{r South_Australia}
South_Australia <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 6,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "South Australia")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```


```{r South_Australia_rate,message=FALSE}
South_Australia_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 6,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
mutate(State = "South Australia")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```


```{r Western_Australia}
Western_Australia <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 7,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Western Australia")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Western_Australia_rate,message=FALSE}
Western_Australia_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 7,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "Western Australia")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```


```{r Tasmania1}
Tasmania1 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", 
                       sheet = 8,range = "A5:AB22")%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))%>%
  select(!("1993":"2009"))%>%
  pivot_longer(cols = '2010':'2013',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(Count = as.double(Count))%>%
 mutate(State = "Tasmania")%>%
  select(Offence,Year,Count,State)
```

```{r Tasmania}
Tasmania2 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", 
                       sheet = 8,range = "A5:AB22")%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))%>%
  select(!("1993":"2013"))%>%
  pivot_longer(cols = '2014':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
 mutate(State = "Tasmania")%>%
 select(Offence,Year,Count,State)
Tasmania <- bind_rows(Tasmania1,Tasmania2)
```

```{r Tasmania_rate1,message=FALSE}
Tasmania_rate1 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 8,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))%>%
  pivot_longer(cols = '2010':'2013',  
               names_to = "Year",            
               values_to = "Rate") %>%
   mutate(Rate = as.double(Rate))%>%
 mutate(State = "Tasmania")%>%
  select(Offence,Year,Rate,State)
```

```{r Tasmania_rate,message=FALSE}
Tasmania_rate2 <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 8,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))%>%
  pivot_longer(cols = '2014':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
 mutate(State = "Tasmania")%>%
  select(Offence,Year,Rate,State)
Tasmania_rate <- bind_rows(Tasmania_rate1,Tasmania_rate2)
```


```{r Northern_Territory}
Northern_Territory <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 9,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Northern Territory")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Northern_Territory_rate,message=FALSE}
Northern_Territory_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "Northern Territory")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```


```{r Capital}
Capital <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A5:AB30")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Count") %>%
  mutate(State = "Capital")%>%
  select(Offence,Year,Count,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction","Armed robbery","	Unarmed robbery","Blackmail/extortion"))
```

```{r Capital_rate,message=FALSE}
Capital_rate <- read_excel("data/Victims_of_Crime_Extended_time_series.xls", sheet = 10,range = "A31:AB41")%>%
  rename("Offence"= "...1","2010" = "...19" , "2011" = "...20","2012" = "...21","2013"="...22","2014"="...23","2015"="...24","2016"="...25","2017"="...26","2018"="...27","2019"="...28")%>%
  pivot_longer(cols = '2010':'2019',  
               names_to = "Year",            
               values_to = "Rate") %>%
  mutate(State = "Capital")%>%
  select(Offence,Year,Rate,State)%>%
  filter(Offence %in%c("Murder","	
Attempted murder","Manslaughter","Sexual assault","Kidnapping/abduction"))
```


```{r State}
State <- bind_rows(NewSouthWales,Victoria,Queensland,South_Australia,Western_Australia,Tasmania,Northern_Territory,Capital)
```

```{r}
State_rate <- bind_rows(NewSouthWales_rate,Victoria_rate,Queensland_rate,South_Australia_rate,Western_Australia_rate,Tasmania_rate,Northern_Territory_rate,Capital_rate)
```


```{r plot1,fig.height = 9, fig.width=10,fig.cap="Comparison of the number of victims of various crimes in each state in the same year"}
State %>%
  ggplot(aes(x = Offence,
             y = Count,
             fill = State))+
  geom_bar(stat  = "identity", position = "dodge") + 
  ggtitle("Comparison of the number of victims of various crimes in each state in the same year") +
  facet_wrap(~Year, ncol = 1,scales= "free")
```

Row {data-height=1000}
-------------------------------------
    
### Chart 2{data-width=500}

```{r plot2,fig.height = 8, fig.width=8, fig.cap="Rate of change in the number of victims of various crimes by state"}
State %>%
  ggplot(aes( x = Year,     
              y = Count, 
              color = Offence,
              group = Offence)) + 
  geom_line(stat = "identity")  +
  theme(axis.title.x =element_text(size=14), axis.title.y=element_text(size=14)) +
   ggtitle("Rate of change in the number of victims of various crimes by state") +
  facet_wrap(~State, ncol = 1,scales= "free")
```


### Chart 3{data-width=550}

```{r}
State_count1<-State %>%
  filter(Offence == "Sexual assault")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("Sexual assault_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
  mutate("Sexual assault_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count2<-State %>%
  filter(Offence == "Murder")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("Murder_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
   mutate("Murder_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count3<-State %>%
  filter(Offence == "Armed robbery")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("robbery_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
   mutate("robbery_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count4<-State %>%
  filter(Offence == "Manslaughter")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("Manslaughter_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
  mutate("Manslaughter_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count5<-State %>%
  filter(Offence == "Kidnapping/abduction")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("Kidnapping_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
   mutate("Kidnapping_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)
State_count6<-State %>%
  filter(Offence == "Blackmail/extortion")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Count)%>%
  mutate("extortion_rate" = ((`2019` - `2010`)/`2010`)*100)%>%
    mutate("extortion_change" = (`2019` - `2010`))%>%
  select(!`2010`:`2019`)

```


```{r table1,fig.height = 4 , fig.width=6}
state_sum1<-merge(State_count1,State_count2, by= 'State')
state_sum<-merge(State_count3,state_sum1, by= 'State')
knitr::kable(state_sum,caption = 'Ten-year rate of change in the number of victims of various crimes by state',booktabs = TRUE)%>%
   kable_styling(bootstrap_options = c("striped", "hover"))
```

```{r table2}
state_sum2<-merge(State_count4,State_count5, by= 'State')
state_sum3<-merge(State_count6,state_sum2,by= 'State')
knitr::kable(state_sum3,caption = 'Ten-year rate of change in the number of victims of various crimes by state',booktabs = TRUE)%>%
   kable_styling(bootstrap_options = c("striped", "hover"))
```

Row {data-height=600}
-------------------------------------

### Chart 4
    
```{r plot3,fig.height = 5 ,fig.width=7,fig.cap="The rate of change in the rate of various crime victims by state"}
State_rate %>%
  filter(Offence == "Sexual assault")%>%
  ggplot(aes( x = Year,     
              y = Rate, 
              color = State,
              group = State)) + 
  geom_line(stat = "identity")  +
  ylab("rate-Victims per 100,000")+
  theme(axis.title.x =element_text(size=14), axis.title.y=element_text(size=14)) +
   ggtitle("Rate of change in the number of victims of various crimes by state") 
```



### Chart 5
   
```{r table3}
State_rate1<-State_rate %>%
  filter(Offence == "Sexual assault")%>%
  pivot_wider(id_cols = State,  
              names_from = Year,  
              values_from = Rate)%>%
  mutate("2010-2011" = (`2019` - `2010`))%>%
  mutate("rate_change" =  ((`2019` - `2010`)/`2010`)*100)%>%
  select(!`2010`:`2019`)
 knitr::kable(
State_rate1, booktabs = TRUE,
  caption = 'A 10-year change in the proportion of victims of sexual assault')%>%
   kable_styling(bootstrap_options = c("striped", "hover"))
```
    


Gender {data-icon="fa-user-plus"}
==================
Column{data-width=400}
--------

### Total number by gender {data-width=350}
  
```{r echo=FALSE}
Victims_of_Crime_raw <- readxl::read_excel("data/Victims_of_Crime_Australia.xls", sheet = 3, skip = 4, col_names = FALSE)
Victims_of_Crime <- Victims_of_Crime_raw[-1,1:11]
colnames(Victims_of_Crime) = Victims_of_Crime[1,]
Victims_of_Crime <- Victims_of_Crime[-1,]
Homicide <- Victims_of_Crime[-1,] %>% 
  slice(c(1:18)) %>% 
  filter(`Sex and age` == "Total")
Homicide[1,1] <- "Homicide_Male"
Homicide[2,1] <- "Homicide_Female"
Homicide[3,1] <- "Homicide_All"
Homicide <- Homicide %>% rename("Type_Gender" = `Sex and age`)
Murder <- Victims_of_Crime %>% 
  slice(c(21:38)) %>% 
  filter(`Sex and age` == "Total")
Murder[1,1] <- "Murder_Male"
Murder[2,1] <- "Murder_Female"
Murder[3,1] <- "Murder_All"
Murder <- Murder %>% rename("Type_Gender" = `Sex and age`)
Attemptedmurder <- Victims_of_Crime %>% 
  slice(c(40:57)) %>% 
  filter(`Sex and age` == "Total")
Attemptedmurder[1,1] <- "Attemptedmurder_Male"
Attemptedmurder[2,1] <- "Attemptedmurder_Female"
Attemptedmurder[3,1] <- "Attemptedmurder_All"
Attemptedmurder <- Attemptedmurder %>% rename("Type_Gender" = `Sex and age`)
Manslaughter <- Victims_of_Crime %>% 
  slice(c(59:76)) %>% 
  filter(`Sex and age` == "Total")
Manslaughter[1,1] <- "Manslaughter_Male"
Manslaughter[2,1] <- "Manslaughter_Female"
Manslaughter[3,1] <- "Manslaughter_All"
Manslaughter <- Manslaughter %>% rename("Type_Gender" = `Sex and age`)
Sexualassault <- Victims_of_Crime %>% 
  slice(c(78:110)) %>% 
  filter(`Sex and age` == "Total")
Sexualassault[1,1] <- "Sexualassault_Male"
Sexualassault[2,1] <- "Sexualassault_Female"
Sexualassault[3,1] <- "Sexualassault_All"
Sexualassault <- Sexualassault %>% rename("Type_Gender" = `Sex and age`)
Kidnappingabduction <- Victims_of_Crime %>% 
  slice(c(112:141)) %>% 
  filter(`Sex and age` == "Total")
Kidnappingabduction[1,1] <- "Kidnappingabduction_Male"
Kidnappingabduction[2,1] <- "Kidnappingabduction_Female"
Kidnappingabduction[3,1] <- "Kidnappingabduction_All"
Kidnappingabduction <- Kidnappingabduction %>% rename("Type_Gender" = `Sex and age`)
Robbery <- Victims_of_Crime %>% 
  slice(c(143:175)) %>% 
  filter(`Sex and age` == "Total")
Robbery[1,1] <- "Robbery_Male"
Robbery[2,1] <- "Robbery_Female"
Robbery[3,1] <- "Robbery_All"
Robbery <- Robbery %>% rename("Type_Gender" = `Sex and age`)
Armedrobbery <- Victims_of_Crime %>% 
  slice(c(177:209)) %>% 
  filter(`Sex and age` == "Total")
Armedrobbery[1,1] <- "Armedrobbery_Male"
Armedrobbery[2,1] <- "Armedrobbery_Female"
Armedrobbery[3,1] <- "Armedrobbery_All"
Armedrobbery <- Armedrobbery %>% rename("Type_Gender" = `Sex and age`)
Unarmedrobbery <- Victims_of_Crime %>% 
  slice(c(211:243)) %>% 
  filter(`Sex and age` == "Total")
Unarmedrobbery[1,1] <- "Unarmedrobbery_Male"
Unarmedrobbery[2,1] <- "Unarmedrobbery_Female"
Unarmedrobbery[3,1] <- "Unarmedrobbery_All"
Unarmedrobbery <- Unarmedrobbery %>% rename("Type_Gender" = `Sex and age`)
Blackmailextortion <- Victims_of_Crime %>% 
  slice(c(245:274)) %>% 
  filter(`Sex and age` == "Total")
Blackmailextortion[1,1] <- "Blackmailextortion_Male"
Blackmailextortion[2,1] <- "Blackmailextortion_Female"
Blackmailextortion[3,1] <- "Blackmailextortion_All"
Blackmailextortion <- Blackmailextortion %>% rename("Type_Gender" = `Sex and age`)
Victims_of_Crime_tidy <- bind_rows(Homicide, Murder, Attemptedmurder, Manslaughter, Sexualassault, Kidnappingabduction, Robbery, Armedrobbery, Unarmedrobbery, Blackmailextortion) %>% 
  mutate(`2010` = as.numeric(`2010`)) %>% 
  pivot_longer(cols = -Type_Gender, 
               names_to = "Year",
               values_to = "Number") %>% 
  separate(col = Type_Gender,
           into = c("Type", "Gender"), "_") %>% 
  filter(!Type == "Homicide",
         !Type == "Robbery")
```

```{r echo=FALSE}
g <- Victims_of_Crime_tidy %>% 
  group_by(Year, Gender) %>%
  summarise(sum = sum(Number)) %>% 
  ggplot(aes(x = Year,
             y = sum,
             color = Gender,
             group = Gender)) +
  geom_line() +
  theme_bw() +
  scale_color_brewer(palette = "Dark2") + 
  theme(legend.position = "bottom")
ggplotly(g)
```


### Analysis{data-width=250}  
  
* On the whole, it shows an upward trend year by year recently.  
* The number of female victims is much higher than that of male victims, almost twice.  

* With the exception of sexual assault and kidnapping, the majority of victims are men.
* The vast majority of cases are sexual assaults.  
* The number of robberies among the remaining crime categories is also considerable. (Including both armed and unarmed)  
* Possible reasons:
  + Women generally have stronger safety awareness
  + Women are less likely to be seen alone at night in areas where they are likely to be robbed  
    
According to statistics of gender-specific crimes in various countries, male victims are the majority in almost all crimes except for sex-related crimes in which more women are victims.
  



Column{data-width=400}
---------------


### Number by gender by crime type {data-width=400}

```{r fig.height = 8, echo=FALSE}
Victims_of_Crime_tidy %>% 
  ggplot() +
  geom_col(aes(x = Year,
                y = Number,
                fill = Gender),
           position = "dodge") +
  facet_grid(Type~., scales = "free_y") +
  scale_fill_brewer(palette = "Dark2") +
  theme_bw()
```




### Sexual assault proportion {data-width=450}



```{r echo=FALSE}
Victims_of_Crime_sum <- Victims_of_Crime_tidy %>% 
  filter(!Gender == "All") %>% 
  group_by(Gender, Year) %>% 
  summarise(sum = sum(Number))

Victims_of_Crime_tidy %>% 
  filter(Type == "Sexualassault" &
         !Gender == "All") %>% 
  left_join(Victims_of_Crime_sum) %>% 
  group_by(Gender, Year) %>% 
  summarise(sa_rate = Number/sum*100) %>% 
  kable(caption = "Sexual Assault Rate") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))
```

Take sexual assault, female victims of sexual assault make up a very high proportion of total female victims.  

Column{data-width=400}
-----------

### Rate by gender{data-width=350}


```{r echo=FALSE}
Victims_of_Crime1 <- Victims_of_Crime_raw[-1,c(1, 12:21)]
colnames(Victims_of_Crime1) = Victims_of_Crime1[1,]
Victims_of_Crime1 <- Victims_of_Crime1[-1,]

Sexualassault1 <- Victims_of_Crime1 %>% 
  slice(c(78:110)) %>% 
  filter(`Sex and age` == "Total")
Sexualassault1[1,1] <- "Sexualassault_Male"
Sexualassault1[2,1] <- "Sexualassault_Female"
Sexualassault1[3,1] <- "Sexualassault_All"
Sexualassault1 <- Sexualassault1 %>% rename("Type_Gender" = `Sex and age`)

Sexualassault_rate <- Sexualassault1 %>% 
  mutate(`2010` = as.numeric(`2010`)) %>% 
  pivot_longer(cols = -Type_Gender, 
               names_to = "Year",
               values_to = "Rate") %>% 
  separate(col = Type_Gender,
           into = c("Type", "Gender"), "_") %>% 
  filter(!Gender == "All")
```


  

```{r echo=FALSE, fig.height=3, fig.width=5}
sa <- Sexualassault_rate %>%
  ggplot() +
  geom_col(aes(x = Year,
               y = Rate,
               fill = Gender),
               position = "dodge") +
  theme_bw() +
  scale_fill_brewer(palette = "Dark2") + 
  theme(legend.position = "bottom")

ggplotly(sa)
```
  
 
### Analysis 
  
* Rate is calculated by victim number divided by 100,000 persons.
* Show an upward pattern over years.
* Higher than 0.15% in recent years which means 3 out of every 2,000 people have been sexually assaulted.
* Possible reasons:
  + Women are physically vulnerable
  + Men are more sexually impulsive
  + Many women may choose not to report the crime
    - relationship to the offender
    - confidence in the justice system
    - fear of revenge
    - public opinion  
  
ABS personal safety survey also measures the number of women who contacted the police about the most recent incident within the last 10 years. Only 13.4% of women did so. 
   







Age {data-icon="fa-user-times"}
====================

Row {data-height=200}
----------------------------------

```{r, echo = FALSE, message = FALSE, warning = FALSE}
Homicide <- read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A20", dim = c(6,11))) 
colnames(Homicide) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Murder <- read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A39", dim = c(6,11)))
colnames(Murder) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Attempted_murder <- read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A58", dim = c(6,11))) 

colnames(Attempted_murder) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Manslaughter <-  read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A77", dim = c(6,11)))

colnames(Manslaughter) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Sexual_assault <- read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A106", dim = c(10,11)))

colnames(Sexual_assault) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Kidnapping_abduction <-  read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A138", dim = c(9,11)))

colnames(Kidnapping_abduction) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Robbery <- read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A171", dim = c(10,11)))

colnames(Robbery) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Armed_robbery <- read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A205", dim = c(10,11)))

colnames(Armed_robbery) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Unarmed_robbery <- read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A239", dim = c(10,11)))

colnames(Unarmed_robbery) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)

Blackmail_extortion <- read_excel("data/Victims_of_Crime_Australia.xls", 
    sheet = "Table 2", range = anchored("A271", dim = c(9,11)))

colnames(Blackmail_extortion) <- c("age" ,2010, 2011, 2012, 2013,	2014,	2015,	2016,	2017,	2018,	2019)
```

```{r Homicide, echo = FALSE, message = FALSE, warning = FALSE}
Homicide %>%
 pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> homicide_long

homicide_long %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) %>%
  mutate(Year = as_factor(Year)) %>%
  mutate(class = "Homicide") -> homicide_wider

homicide_long %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Homicide") %>%
  filter(age != "Total") -> homicide_age

homicide_age %>%
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) +
  ggtitle("Homicide")-> p1
```


``` {r Murder, echo = FALSE, message = FALSE, warning = FALSE}
Murder %>%
pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> murder_longer

murder_longer %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) %>%
  mutate(Year = as_factor(Year)) %>%
  mutate(class = "Murder") -> murder_wider

murder_longer %>%
   group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Murder") %>%
  filter(age != "Total") -> murder_age

murder_age %>%
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) +
  ggtitle("Murder") -> p2
```

``` {r Attempted_murder, echo = FALSE, message = FALSE, warning = FALSE}
Attempted_murder %>%
  pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> AM_longer

AM_longer %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) %>%
  mutate(class = "Attempted") -> AM_wider

AM_longer %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Attempted") %>%
  filter(age != "Total") -> am_age

am_age %>%
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) +
  ggtitle("Attempted_murder") -> p3
```

``` {r Manslaughter, echo = FALSE, message = FALSE, warning = FALSE}
Manslaughter %>%
  pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> manslaughter_longer

manslaughter_longer %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) %>%
  mutate(Year = as_factor(Year)) %>%
  mutate(class = "Manslaughter") -> manslaughter_wider

AM_longer %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Manslaughter") %>%
  filter(age != "Total") -> manslaughter_age
manslaughter_age %>% 
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) +
  ggtitle("Manslaughter") -> p4
```

``` {r Sexual_assault, echo = FALSE, message = FALSE, warning = FALSE}
Sexual_assault %>%
  pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> SA_longer_edit

SA_longer_edit %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) -> SA_wider_edit

SA_wider_edit %>%
  mutate(
    `0–19 years` = `0–9 years` + `10–14 years` + `15–19 years`,
    `20–34 years` = `20–24 years` + `25–34 years`,
    `35–54 years` = `35–44 years` + `45–54 years`,
    `55 years and over` = `55–64 years`+`55–64 years`+ `65 years and over`
  ) %>%
  select(Year, `0–19 years`:`55 years and over`)  -> SA_wider

SA_wider %>%
  pivot_longer(cols = -Year, names_to = "age", values_to = "Count") %>%
  mutate(class = "Sexual") -> SA_longer

SA_longer %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Sexual") %>%
  filter(age != "Total") -> SA_age

SA_age%>%
  ggplot() + geom_col(aes(x = age ,y = sum, fill = age)) + 
  ggtitle("Sexual assault") -> p5
```

``` {r Kidnapping_abduction, echo = FALSE, message = FALSE, warning = FALSE}
Kidnapping_abduction %>%
  pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> ka_longer_edit

ka_wider_edit <- ka_longer_edit %>% 
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) 
  
ka_wider_edit %>%
   mutate(
    `0–19 years` = `0–9 years` + `10–14 years` + `15–19 years`,
    `20–34 years` = `20–24 years` + `25–34 years`,
    `35–54 years` = `35–44 years` + `45–54 years`,
    `55 years and over` = `55 years and over`
  ) %>%
  select(Year, `0–19 years`, `20–34 years`, `35–54 years`, `55 years and over`) -> ka_wider

ka_wider %>%
  pivot_longer(cols = -Year, names_to = "age", values_to = "Count") -> ka_longer
  

ka_longer %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Kidnapping") %>%
  filter(age != "Total") -> ka_age
ka_age %>%
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) +
  ggtitle("Kidnapping abduction") -> p6
```

``` {r Robber, echo = FALSE, message = FALSE, warning = FALSE}
Robbery %>%
   pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> Robbery_longer_edit

Robbery_longer_edit %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) %>%
  mutate(Year = as_factor(Year)) %>%
  mutate(class = "robbery") -> Robbery_wider_edit

Robbery_wider_edit %>%
  mutate(
    `0–19 years` = `0–9 years` + `10–14 years` + `15–19 years`,
    `20–34 years` = `20–24 years` + `25–34 years`,
    `35–54 years` = `35–44 years` + `45–54 years`,
    `55 years and over` = `55–64 years`+`55–64 years`+ `65 years and over`
  ) %>%
  select(Year, `0–19 years`:`55 years and over`) -> Robbery_wider

Robbery_wider %>%
  pivot_longer(cols = -Year, names_to = "age", values_to = "Count") %>%
  mutate(class = "robbery") -> Robbery_longer
  

Robbery_longer %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "robbery") %>%
  filter(age != "Total") -> robbery_age
robbery_age %>% 
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) +
  ggtitle("Robber") -> p7
```

``` {r Armed_robbery, echo=FALSE, message = FALSE, warning = FALSE}
Armed_robbery %>%
  pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> AR_longer_edit

AR_longer_edit %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) %>%
  mutate(Year = as_factor(Year)) -> AR_wider_edit

AR_wider_edit %>%
  mutate(
    `0–19 years` = `0–9 years` + `10–14 years` + `15–19 years`,
    `20–34 years` = `20–24 years` + `25–34 years`,
    `35–54 years` = `35–44 years` + `45–54 years`,
    `55 years and over` = `55–64 years`+`55–64 years`+ `65 years and over`
  ) %>%
  select(Year, `0–19 years`:`55 years and over`) -> AR_wider

AR_wider %>%
  pivot_longer(cols = -Year, names_to = "age", values_to = "Count") %>%
  mutate(class = "Armed") -> AR_longer

AR_longer %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Armed") %>%
  filter(age != "Total") -> AR_age
AR_age %>% 
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) + 
  ggtitle("Armed robbery") -> p8
```

``` {r Unarmed_robbery, echo = FALSE, message = FALSE, warning = FALSE}
Unarmed_robbery %>%
  pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> UR_longer_edit

UR_longer_edit %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) %>%
  mutate(Year = as_factor(Year)) %>%
  mutate(class = "Unarmed") -> UR_wider_edit

UR_wider_edit %>%
  mutate(
    `0–19 years` = `0–9 years` + `10–14 years` + `15–19 years`,
    `20–34 years` = `20–24 years` + `25–34 years`,
    `35–54 years` = `35–44 years` + `45–54 years`,
    `55 years and over` = `55–64 years`+`55–64 years`+ `65 years and over`
  ) %>%
  select(Year, `0–19 years`:`55 years and over`) -> UR_wider

UR_wider %>%
  pivot_longer(cols = -Year, names_to = "age", values_to = "Count") %>%
  mutate(class = "Unarmed") -> UR_longer

UR_longer %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Unarmed") %>%
  filter(age != "Total") -> UR_age

UR_age %>%
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) +
  ggtitle("Unarmed robbery") -> p9
```

``` {r Blackmail_extortion, echo = FALSE, message = FALSE, warning = FALSE}
Blackmail_extortion %>%
  pivot_longer(cols = '2010':'2019',  
                names_to = "Year",            
                values_to = "Count") -> BE_longer_edit

BE_longer_edit %>%
  pivot_wider(id_cols = c(age, Year, Count), names_from = age, values_from = Count) %>%
  mutate(Year = as_factor(Year)) %>%
  mutate(class = "Blackmail") -> BE_wider_edit

BE_wider_edit %>%
  mutate(
    `0–19 years` =  `0–14 years` + `15–19 years`,
    `20–34 years` = `20–24 years` + `25–34 years`,
    `35–54 years` = `35–44 years` + `45–54 years`,
    `55 years and over` = `55–64 years`+`55–64 years`+ `65 years and over`
  ) %>%
  select(Year, `0–19 years`:`55 years and over`) -> BE_wider

BE_wider %>%
  pivot_longer(cols = -Year, names_to = "age", values_to = "Count") %>%
  mutate(class = "Blackmail") -> BE_longer
  

BE_longer %>%
  group_by(age) %>%
  summarise(sum = sum(Count)) %>%
  mutate(class = "Blackmail") %>%
  filter(age != "Total") -> BE_age
BE_age %>% 
  ggplot() + geom_col(aes(x= age, y = sum, fill = age)) + 
  ggtitle("Blackmail_extortion") -> p10
```

```{r}
homicide_age %>%
  full_join(robbery_age, key = age, by = c("age", "sum", "class"))%>%
  full_join(ka_age, key = age, by = c("age", "sum", "class")) %>% 
  full_join(am_age, key = age, by = c("age", "sum", "class")) %>%
  full_join(murder_age, key = age, by = c("age", "sum", "class")) %>%
  full_join(manslaughter_age, key = age, by = c("age", "sum", "class")) %>%
  full_join(SA_age, key = age, by = c("age", "sum", "class")) %>%
  full_join(AR_age, key = age, by = c("age", "sum", "class")) %>%
  full_join(UR_age, key = age, by = c("age", "sum", "class")) %>%
  full_join(BE_age, key = age, by = c("age", "sum", "class"))-> data
```

```{r}
AM_wider$`0–19 years` + AR_wider$`0–19 years`+ BE_wider$`0–19 years` + homicide_wider$`0–19 years` + ka_wider$`0–19 years` + manslaughter_wider$`0–19 years`+murder_wider$`0–19 years`+Robbery_wider$`0–19 years` + SA_wider$`0–19 years`+ UR_wider$`0–19 years` -> teenage

AM_wider$`20–34 years`  + AR_wider$`20–34 years` + BE_wider$`20–34 years` + homicide_wider$`20–34 years` + ka_wider$`20–34 years` + manslaughter_wider$`20–34 years` + murder_wider$`20–34 years` + Robbery_wider$`20–34 years` + SA_wider$`20–34 years` + UR_wider$`20–34 years` -> adult

AM_wider$`35–54 years`  + AR_wider$`35–54 years` + BE_wider$`35–54 years` + homicide_wider$`35–54 years` + ka_wider$`35–54 years` + manslaughter_wider$`35–54 years` + murder_wider$`35–54 years` + Robbery_wider$`35–54 years` + SA_wider$`35–54 years` + UR_wider$`35–54 years` -> elder_adult

AM_wider$`55 years and over`  + AR_wider$`55 years and over` + BE_wider$`55 years and over` + homicide_wider$`55 years and over` + ka_wider$`55 years and over` + manslaughter_wider$`55 years and over` + murder_wider$`55 years and over` + Robbery_wider$`55 years and over` + SA_wider$`55 years and over` + UR_wider$`55 years and over` -> elder
```


### Age Group
I divided age into four groups

* 0 -19 years old Teenage

* 20–34 years old Audlt

* 35–54 years old Elder audlt

* 55 years and over Elder

Row
----------------------------------

### Trend
```{r}
Year <- 2010:2019
tibble(Year, teenage, adult, elder_adult, elder) -> age_total
age_total %>%
  mutate(Year = as.numeric(Year)) -> age

ggplot() +
  geom_line(aes(x=Year, y = teenage), data =age %>% select(Year, teenage)) +
  ggtitle("Teenage") -> p_teenage
ggplot() +
  geom_line(aes(x=Year, y = adult), data = age %>% select(Year, adult)) +
  ggtitle("adult") -> p_adult
ggplot() +
  geom_line(aes(x=Year, y = elder_adult), data = age %>% select(Year, elder_adult)) +
  ggtitle("elder_adult") -> p_elder_adult
ggplot() +
  geom_line(aes(x=Year, y = elder), data =age %>% select(Year, elder)) +
  ggtitle("elder") -> p_elder
grid.arrange(p_teenage, p_adult, p_elder_adult, p_elder)
```

Row {data-height=80}
----------------------------------
### Analysis

From graph, we can see that the trend for both teenage and adult is very similar. it has a decrease trend from 2010 to 2015. Then, it has a increse trend from 2015 to 2019. For the elder adult and elder, they have increase trend for all year.

Row 
----------------------------------

### Age and Class
```{r}
data %>%
  ggplot() + geom_col(aes(x= class, y = sum, fill = age), position = "fill") -> p_hist
plotly::ggplotly(p_hist)
```

Row 
----------------------------------
### Analysis

From graph. we can see that 20-54 years age are the largest people for criminal record. The 55 year and over are the smallest group.


Row 
--------------------------------------

```{r}
p1
```

```{r}
p2
```

```{r}
p3
```

Row 
--------------------------------------

```{r}
p4
```

```{r}
p5
```

```{r}
p6
```

Row 
--------------------------------------

```{r}
p7
```

```{r}
p8
```

```{r}
p9
```


Row 
--------------------------------------

```{r}
p10
```